Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)

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Enhancing Facial Transformation Capabilities: Synthetic Child Facial Data Generation and Validation

Authors
K. Lahari1, C. Shoba Bindu2, O. Roopa Devi3, *
1Department of CSE (AI), JNTUA College of Engineering Ananthapuramu, Anantapur, India
2Department of CSE, JNTUA College of Engineering Ananthapuramu, Anantapur, India
3Department of CSE, G. Pulla Reddy Engineering College, Kurnool, India
*Corresponding author. Email: roopaodem.ecs@gprec.ac.in
Corresponding Author
O. Roopa Devi
Available Online 17 March 2025.
DOI
10.2991/978-94-6463-662-8_79How to use a DOI?
Keywords
Generative AI; Synthetic Child Facial Data; Facial Transformations; ChildGAN; Computer Vision; Diffusion Models
Abstract

The rise of generative AI has significantly impacted content creation, facilitating the rapid generation of high-quality text, images, audio, and synthetic data. This study investigates the generation of synthetic facial data for children, enabling complex facial transformations such as expression changes, age progression, eye blinking, head pose variations, and modifications in skin and hair color under varying lighting conditions. Our dataset consists of over 300,000 unique samples sourced from ChildGAN and real-world images obtained from the Children's Vision Network. To evaluate the quality and distinctiveness of the generated facial features, we employ a variety of computer vision methodologies, including a CNN- based child gender classifier, face localization, facial landmark detection, identity similarity analysis using ArcFace, and assessments of eye detection and aspect ratio. The results demonstrate that high-quality synthetic facial data can effectively mitigate the challenges of collecting extensive datasets from real children. Furthermore, this research aims to improve data augmentation techniques by utilizing diffusion models to generate child data samples that accurately represent ethnic diversity and various racial backgrounds.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)
Series
Advances in Engineering Research
Publication Date
17 March 2025
ISBN
978-94-6463-662-8
ISSN
2352-5401
DOI
10.2991/978-94-6463-662-8_79How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - K. Lahari
AU  - C. Shoba Bindu
AU  - O. Roopa Devi
PY  - 2025
DA  - 2025/03/17
TI  - Enhancing Facial Transformation Capabilities: Synthetic Child Facial Data Generation and Validation
BT  - Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)
PB  - Atlantis Press
SP  - 1017
EP  - 1031
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-662-8_79
DO  - 10.2991/978-94-6463-662-8_79
ID  - Lahari2025
ER  -